We compared Amazon SageMaker and Azure OpenAI based on our user's reviews in several parameters.
Amazon SageMaker provides users with efficient model training and deployment, seamless integration with AWS services, and strong customer support. On the other hand, Azure OpenAI offers seamless integration with Azure services, flexible scaling options, and valuable insights for decision-making. Both products receive positive feedback for their pricing, setup process, and ROI, but users have identified areas for improvement.
Features: Amazon SageMaker is highly valued for its ease of use, comprehensive machine learning capabilities, customizable workflows, automated data labeling, and robust monitoring and troubleshooting tools. On the other hand, Azure OpenAI is praised for its seamless integration with Azure services, scalability, robust machine learning capabilities, and excellent documentation and support.
Pricing and ROI: Amazon SageMaker's setup cost is deemed reasonable and straightforward, with clear and transparent licensing. On the other hand, Azure OpenAI is positively regarded for its minimal setup cost, smooth process, and adaptable licensing options, providing cost-efficiency and meeting varying user requirements., Amazon SageMaker has been praised for its positive ROI, providing benefits and value. Azure OpenAI offers increased efficiency and productivity, cost reduction, improved business performance, and valuable insights for decision-making.
Room for Improvement: Users have identified areas where Amazon SageMaker could be enhanced. Many users have provided feedback on ways to enhance Azure OpenAI. They have voiced concerns regarding certain functions and suggested improvements.
Deployment and customer support: Amazon SageMaker: User reviews indicate varying durations for establishing a new tech solution, with some users spending three months on deployment and an additional week on setup, while others mentioned a week for both deployment and setup. Azure OpenAI: Users reported spending three months on deployment and an additional week on setup, suggesting that both timeframes should be considered. Another user required a week for both deployment and setup, indicating that these terms refer to the same period and should not be considered separately., Amazon SageMaker's customer service and support are praised for their helpfulness and responsiveness, efficiency, and promptness in issue resolution. Users appreciate the support team's attentiveness and commitment to addressing customer needs. In comparison, Azure OpenAI's customer service is highly regarded for exceptional assistance, efficient handling of queries, and ensuring a smooth user experience.
The summary above is based on 21 interviews we conducted recently with Amazon SageMaker and Azure OpenAI users. To access the review's full transcripts, download our report.
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We were able to use the product to automate processes."
"The few projects we have done have been promising."
"Allows you to create API endpoints."
"They are doing a good job of evolving."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."
"Generative AI or GenAI seems to be the best part of the solution."
"The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"You just have to write accurate prompts according to your requirements, and the solution gives very good results."
"We can use the solution to implement our tasks and models quickly."
"The product is easy to integrate with our IT workflow."
"Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"The product must provide better documentation."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The solution requires a lot of data to train the model."
"AI is a new area and AWS needs to have an internship training program available."
"Lacking in some machine learning pipelines."
"The solution needs to be cheaper since it now charges per document for extraction."
"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"The solution needs to accommodate smaller companies."
"Our customers are worried about data management, ethical, and security issues."
"Azure OpenAI is not available in all regions, and its technical support should be improved."
"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."
"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."
"One area for improvement is providing more flexibility in configuration and connectivity with external tools."
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while Azure OpenAI is ranked 2nd in AI Development Platforms with 23 reviews. Amazon SageMaker is rated 7.4, while Azure OpenAI is rated 8.0. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Azure OpenAI writes "Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions". Amazon SageMaker is most compared with Databricks, Google Vertex AI, Domino Data Science Platform, Microsoft Azure Machine Learning Studio and Dataiku, whereas Azure OpenAI is most compared with Google Vertex AI, Microsoft Azure Machine Learning Studio, Hugging Face, Google Cloud AI Platform and IBM Watson Studio. See our Amazon SageMaker vs. Azure OpenAI report.
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